Predictive Regression with p-Lags and Order-q Autoregressive Predictors

29 Pages Posted: 8 Jun 2020

See all articles by Harshanie L. Jayetileke

Harshanie L. Jayetileke

Queensland University of Technology

You-Gan Wang

Queensland University of Technology

Min Zhu

University of Queensland

Date Written: April 10, 2020

Abstract

This paper considers predictive regressions, where yt is predicted by all p lags of x, here with x being autoregressive of order q, PR(p,q). The literature considers model properties in the cases where p=q. We demonstrate that the current augmented regression method can still reduce the bias in predictive coefficients, but its efficiency depends on correctly specifying both p and q. We propose an estimation framework for the predictive regression, PR(p,q), with a data-driven auto-selection of p and q to achieve the best bias reduction in predictive coefficients. The corresponding hypothesis testing procedure is also derived. The efficiency of the proposed method is demonstrated with simulations. Empirical applications to equity premium prediction illustrate the substantial difference between the estimates of our method and those obtained by the common predictive regressions with p=q.

Keywords: Predictive regressions, bias, augmented regression, return predictability

JEL Classification: C14, G12

Suggested Citation

Jayetileke, Harshanie L. and Wang, You-Gan and Zhu, Min, Predictive Regression with p-Lags and Order-q Autoregressive Predictors (April 10, 2020). Available at SSRN: https://ssrn.com/abstract=3597761 or http://dx.doi.org/10.2139/ssrn.3597761

Harshanie L. Jayetileke

Queensland University of Technology ( email )

2 George Street
Brisbane, Queensland 4000
Australia

You-Gan Wang

Queensland University of Technology ( email )

2 George Street
Brisbane, Queensland 4000
Australia

Min Zhu (Contact Author)

University of Queensland ( email )

St Lucia
Brisbane, Queensland 4072
Australia

HOME PAGE: http://https://www.business.uq.edu.au/staff/min-zhu

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